Faculty Publications

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Subject: search.filters.subject.Radial distribution networks

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    Distributed load flow analysis using graph theory
    (2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.
    In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.
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    Distributed load flow analysis using graph theory
    (2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.
    In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.
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    Online voltage estimation and control for smart distribution networks
    (2016) Raghavendra, P.; Gaonkar, D.N.
    The increasing deployment of Distributed Generation (DG) technologies introduces power quality challenges to the grid, in particular steady state voltage rise at the connection point forDGunits. In most distribution networks, control and monitoring of grid parameters is missing, as well as system security is at risk. Smart grid technologies have the capability to realize the real-time measurements and on-load voltage controls. With the steady implementation of smart grid technologies throughout the existing distribution networks, the online voltage control can be achieved ensuring the power quality and voltage levels within the statutory limits. This study presents a methodology for the estimation of voltage profile in a smart distribution network with DG for the online voltage control, taking into account different line X/R ratios and laterals. This method is based on maximum and minimum voltage estimation by remote terminal units (RTUs) placed only at DG connected bus and at capacitor connected bus. Voltage regulation is carried out based on RTUs estimated values. This work is tested on two radial distribution networks with/without DGs and laterals. Comparative results for voltage magnitudes estimated with different methodology are presented. The reported simulation results show that the method presented is capable of estimating the voltage profile along the distribution network with DGs for the online voltage control, considering different line X/R ratios and laterals. © The Author(s) 2016.
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    Faster load flow algorithm for radial distribution network using graph theory
    (John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2019) Sharma, D.P.; Chaturvedi, A.; Saxena, R.; Raguru, J.
    Since last 3 decades, load flow solutions have enjoyed success on different fronts. Primarily, the importance and utility of these algorithms is assessed using performance measures, which usually include issue like implementation complicacy, optimized execution time, and memory storage. In this work, a graph-theoretical approach is used to facilitate load flow solutions for a static network topology. Algorithm is tested for 2 different radial distribution topologies, and its deployment for both of these network finally results in phenomenal saving on 2 important algorithm performance measures, ie, time and space complexity. Obtained phenomenal saving for both of these 2 parameters is compared with earlier reported work on statistical basis. © 2018 John Wiley & Sons, Ltd.